Data Engineer

Are you passionate about building scalable data solutions and working with modern cloud technologies? Do you enjoy developing efficient data pipelines, optimising data workflows, and finding better ways to manage and process complex datasets?

to join ATI Business Group. In this role, you'll design, develop, and maintain our Global Supply data platform using

Databricks and Microsoft Azure, building reliable data pipelines and reusable engineering frameworks to support efficient and well-governed data operations.

We're looking for someone with strong hands-on experience in

Databricks, PySpark, SQL, and Azure data services, particularly in production environments, who is keen to contribute to the development and continuous improvement of modern data engineering solutions.

Responsibilities

• Design, develop, and maintain scalable data pipelines to ingest, transform, and process data from internal and external sources using Databricks and
• Medallion Architecture (Bronze, Silver, and Gold).
• Build and manage data ingestion, storage, and orchestration workflows across cloud environments, preferably using
• Azure Data Factory (ADF)
• Azure Data Lake Storage Gen2 (ADLS), and Delta Lake

Develop and maintain production-ready ETL processes using

• PySpark, Python, and SQL, ensuring data quality, reliability, and efficient processing.
• Optimise data pipelines, Spark workloads, and storage solutions to improve performance, scalability, and cost efficiency.
• reusable, metadata-driven engineering frameworks
• to standardise data ingestion, transformation, error handling, logging, and data quality processes.
• Implement and maintain
• CI/CD pipelines, version control, and automated deployment processes using
• Azure DevOps and Git
• Collaborate with technical teams and stakeholders to understand data requirements, address engineering challenges, and continuously improve data platform capabilities.
• Is this you?

Requirements

Bachelor's degree in

• Computer Science, Information Technology, Information Systems, Software Engineering, or a related technical field.
• 3 years of relevant experience in Data Engineering, particularly in developing and maintaining production data pipelines.
• Strong, hands-on experience with

Databricks, including

• Apache Spark, PySpark, SQL, Delta Lake, and Unity Catalog
• Experience working with cloud platforms such as Microsoft Azure, AWS, or GCP, with Azure experience being highly preferred.
• Familiarity with cloud-based data services and ETL tools, particularly
• Azure Data Factory (ADF) and Azure Data Lake Storage Gen2 (ADLS)

Strong programming skills in

• Python and SQL, with the ability to develop, troubleshoot, and optimise data processing solutions.
• Git and CI/CD practices, including version control, branching strategies, and automated deployment of data solutions.
• Good understanding of data modelling, ETL/ELT processes, and Lakehouse architecture, including
• Medallion Architecture.
• Databricks Lakeflow, including

Exposure to

• Lakebase, Databricks Apps, or Genie (AI/BI), or an interest in exploring emerging Databricks technologies, is a plus.
• Strong analytical and problem-solving skills, attention to detail, and the ability to collaborate effectively with technical teams and stakeholders.

Preferred

• Experience designing reusable, metadata-driven data engineering frameworks, with an understanding of modular development, testing, logging, and error handling, is a plus.
• Declarative Pipelines, Jobs, or Connect, is an advantage